Discover
Understand your goals, workflows, and operating environment.
↗AI
LLM infrastructure shaped around your data and policies.
Assess models, infrastructure, access controls, and operations for on-premises, dedicated cloud, or restricted-network deployments. A private deployment generally means running a suitable existing model in your environment—not training a foundation model from scratch.
01SCOPE OF WORK
A scope made
clear.
01
Model assessment against tasks, languages, data needs, and licensing
02
Capacity planning based on concurrency, context length, and latency targets
03
Inference services, access APIs, and internal interfaces
04
Internal retrieval, access policies, and identity integration
05
Usage, quality, and incident monitoring with operational handover
Deliverables, integration responsibilities, timing, and operational support are agreed for each project. Third-party service fees and licenses are reviewed separately.
We define data access, evaluation criteria, and tasks that require human approval or review.
02HOW WE WORK
Define
Define features, priorities, and integration requirements.
Build
Build the agreed scope and review progress together.
Launch & Operate
Prepare testing, deployment, and operational handover.
↗YOUR NEXT CHAPTER
Let’s build the ground
for what comes next.
You don’t need a finished specification.
Start with the problem you want to solve.